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Principle and application of three-band gradient difference vegetation index

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Science China Earth Sciences 2005年第2期48卷 241-249页

作者： TANG Shihao, ZHU Qijiang, wang jindi, ZHOU Yuyu & ZHAO Feng research {3. for remote sensing and gis, Department of Geography, beijing normal University, beijing 100875, China National Satellite Meteorological {3., China Meteorological Administration, beijing 100081, China 1. research center for remote {3. and gis Department of Geography Beijing Normal University 100875 Beijing China 2. National Satellite Meteorological center China Meteorological Administration 100081 Beijing China

Vegetation index is a simple, effective and experiential measurement of terrestrial vegetation activity, and plays a very important role in qualitative and quantitative remote sensing. Aiming at shortages of current vegetation indices, and starting from the analysis of vegetation spectral characteristics, we put forward a new vegetation index, the three-band gradient difference vegetation index (TGDVI), and established algorithms to inverse crown cover fraction and leaf area index (LAI) from it. Theoretical analysis and model simulation show that TGDVI has high saturation point and the ability to remove the influence of background to some degree, and the explicit functional relation with crown cover fraction and LAI can be established. Moreover, study shows that TGDVI also has the ability to partly remove the influence of thin cloud. Experiment in the Shunyi District, beijing, China shows that reasonable result can be reached using the vegetation index to retrieve LAI. We also theoretically analyzed the reason why the {3.ized difference vegetation index (NDVI) owns the low saturation point, and show that it is determined by the definition of NDVI and the characteristic of vegetation spectra, and is unavoidable to some degree. Meanwhile, through model simulation, we also indicate that the relationship between simple ratio vegetation index (SR) and LAI closes to a piecewise linear one instead of a linear one, which is mainly caused by the influence of background and different change rates of reflectance in red and infrared bands with LAI increasing.

关键词： vegetation index, remote sensing, LAI, crown cover fraction, remote sensing inversion.

Comments on scaling effect of reciprocity in BRDF study

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Progress in Natural Science:Materials International 2004年第3期 95-96页

作者： ZHAO Hongrui* and wang jindi(research {3. for remote sensing and gis, Department of Geography, beijing normal University, beijing Key Laboratory for remote sensing of Environment and Digital Cities, beijing {13., China) researchcenterforremote{3.andgis DepartmentofGeographyBeijingNormalUniversityBeijingKeyLaboratoryforRemoteSensingofEnvironmentandDigitalCitiesBeijing100875China

Reciprocity principle is a common theory in electromagnetics and optics. It is also one of the general principles of the radiation transformation theory. However, in many remote sensing studies, this principle cannot be always supported by experimental data, especially when the scaling effect of the remote sensing image pixel is considered. The debate over the issue of whether the reciprocity principle can be used as a fundamental standard in evaluating the effectiveness of remote sensing observations has lasted for years. Using geometrical optics model, li and Wan proved the existence of the scaling effect in applying the reciprocity principle to a remote sensing image pixel in 1998. In 2002, Snyder challenged li’s proof, and attempted to prove that reciprocity principle is universally valid without any scale limitation. In this paper, we will argue with Snyder’s theory and point out that Snyder’s theory had neglected an important condition used in li’s proof and, as a result, drawn the wrong conclusion. Here we will restate li’s condition in his demonstration and offer a further proof to verify that reciprocity principle cannot be applied unconditionally to the study of bi directional reflectance distribution function (BRDF) of land surfaces in field or satellite remote sensing observation scale.

关键词： BRDF reciprocity scaling effect.

Application of ensemble kalman filter to geophysical parameters retrieval in remote sensing:A case study of kernel-driven BRDF model inversion

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Science China Earth Sciences 2006年第6期49卷 632-640页

作者： QIN Jun1, YAN Guangjian1, liU Shaomin1, liANG Shunlin2, ZHANG Hao1, {3. jindi1 & li xiaowen1 1. State Key Laboratory of {3. sensing Science, School of Geography and {3. sensing, research center for RS&{13., beijing normal University, beijing 100875, China 2. Department of Geography, 2181 Lefrak Hall, University of Maryland, College Park, MD20742, USA 1. State Key Laboratory of remote sensing Science School of Geography and Remote Sensing Research Center for RS&GIS Beijing Normal University Beijing 100875 China 2. Department of Geography 2181 Lefrak Hall University of Maryland College Park MD20742 USA

The use of a priori knowledge in remote sensing inversion has great implications for ensuring the stability of inversion process and reducing uncertainties in retrieved results, especially under the condition of insufficient observations. Common optimization algorithms have difficulties in providing posterior distribution and thus cannot directly acquire uncertainties in inversion results, which is of no benefit to remote sensing application. In this article, ensemble Kalman filter (EnKF) has been introduced to retrieve surface geophysical parameters from remote sensing observations, which has the capability of not merely obtaining inversion results but also giving its posterior distribution. To show the advantage of EnKF, it is compared to standard MODIS AMBRALS algorithm and highly effi-cient global optimization method SCE-UA. The inversion abilities of kernel-driven BRDF models with different kernel combinations at several main cover types are emphatically discussed when observa-tions are deficient and a priori knowledge is introduced into inversion.

关键词： remote sensing inversion, a priori knowledge, posterior distribution, ensemble kalman filter, BRDF, kernel-driven model, albedo.

Bi-directional normalized difference vegetation index: concept and application

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Progress in Natural Science:Materials International 2002年第2期 37-41页

作者： li xiaowen , GAO Feng , wang {3. , YANG Hua, A. STRAHLER and C. SCHAAF( {3. center for remote sensing and Department of Geography, beijing normal University, beijing 100875, China Department of Geography and center for remote sensing, Boston University, Boston MA02215, USA) research center for remote {3. and Department of Geography，beijing normal University，beijing 100875，China Department of Geography and center for remote sensing，Boston University，Boston MA02215，USA Department of Geography and center for remote sensing，Boston University，Boston MA02215，USA research center for remote {3. and Department of Geography，beijing normal University，beijing 100875，China

The data products of land surface bi-directional reflectance distribution function (BRDF) from the space-borne Polarization and Directionality of the Earth Reflectance (POLDER) of France and the Moderate Resolution Imaging Spectroradiometer (MODIS) of USA are available recently, but the atmospheric correction for meeting the requirement of quantitative remote sensing is still a very difficult problem. This paper presents a concept: bi-directional {3.ized difference vegetation index (Bi-NDVI), in order to consider simultaneously the effects of both land surface BRDF and atmospheric path scattering. An atmospheric quality index is thus defined for satellite multi-angular observations. The quality of MODIS/BRDF data products can be improved notably through iterative inversion weighed by this index.

关键词： bi-directional reflectance normalized difference vegetation index atmospheric correction.

Chapter 12 Numerical Inversion Methods in Geoscience and Quantitative remote sensing

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Chapter 12 Numerical Inversion Methods in Geoscience and Qua...

On the effective inversion by imposing a priori information for retrieval of land surface parameters

中国科学院地质与地球物理研究所第十届(2010年度)学术年会

作者： Yanfei wang Institute of Geology and Geophysics,Chinese Academy of Sciences,beijing 100029,China. xiaowen {3. {3. center for remote sensing and gis,beijing normal University,beijing,100875, China.

To estimate structural parameters and spectral component signatures of Earth surface cover type,quantitative remote sensing seems to be an appropriate way to deal with these *** the real physical system that couples the atmosphere with the land surface is very complicated and should be continuous,sometimes it requires comprehensive parameters to describe such a system,so any practical physical model can only be approximated by a model which includes only a limited number of the most important parameters that capture the major variation of the real system. The pivot problem for quantitative remote sensing is the *** problems are typically *** ill-posed nature is characterized by(C1) the solution may not exist;(C2) the dimension of the solution space may be infinite;(C3. the solution is not continuous with the variation of the observed *** issues exist for all quantitative remote sensing inverse problems. For example,when sampling is poor,i.e.,there are very few observations,or directions are poorly located,the inversion process would be underdetermined, which leads to the large condition number of the normalized systems and the significant noise ***(C2) and(C3. would be the chief difficulties for quantitative remote sensing inversion. This chapter will address the theory and methods from the viewpoint that the quantitative remote sensing inverse problems can be represented by kernel-based operator equations.

关键词：

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Science China Earth Sciences 2009年第4期52卷 540-549页

作者： wang YanFei MA ShiQian YANG Hua wang jindi {3. {3. Key Laboratory of Petroleum Geophysics Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of remote sensing Science Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications of Chinese Academy of SciencesBeijing 100875China Department of Industrial Engineering and Operations research Columbia UniversityNew YorkNY 10027-6902USA

The anisotropy of the land surface can be best described by the bidirectional reflectance distribution function (BRDF). As the field of multiangular remote sensing advances, it is increasingly probable that BRDF models can be inverted to estimate the important biological or climatological parameters of the earth surface such as leaf area index and albedo. The state-of-the-art of BRDF is the use of the linear kernel-driven models, mathematically described as the linear combination of the isotropic kernel, volume scattering kernel and geometric optics kernel. The computational stability is characterized by the algebraic operator spectrum of the kernel-matrix and the observation errors. Therefore, the retrieval of the model coefficients is of great importance for computation of the land surface albedos. We first consider the smoothing solution method of the kernel-driven BRDF models for retrieval of land surface albedos. This is known as an ill-posed inverse problem. The ill-posedness arises from that the linear kernel driven BRDF model is usually underdetermined if there are too few looks or poor directional ranges, or the observations are highly dependent. For example, a single angular observation may lead to an under-determined system whose solution is infinite (the null space of the kernel operator contains nonzero vectors) or no solution (the rank of the coefficient matrix is not equal to the augmented matrix). Therefore, some smoothing or regularization technique should be applied to suppress the ill-posedness. So far, least squares error methods with a priori knowledge, QR decomposition method for inversion of the BRDF model and regularization theories for ill-posed inversion were developed. In this paper, we emphasize on imposing a priori information in different spaces. We first propose a general a priori imposed regularization model problem, and then address two forms of regularization scheme. The first one is a regularized singular value decomposition method

关键词： ill-posed problems land surface parameter retrieval optimization regularization

An improved TES algorithm based on the corrected ALPHA difference spectrum

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Science China Earth Sciences 2007年第2期50卷 274-282页

作者： TANG ShiHao li xiaowen wang {3. ZHU QiJiang ZHANG liHua National Satellite Meteorological center Key Laboratory of Radiometric Calibration and Validation for Environmental SatellitesChinese Meteorological AdministrationBeijing 100081China research center of remote {3.&gis State Key Lab Remote Sensing ScienceBeijing Normal UniversityBeijing 100875China

Different from visible signals, thermal infrared radiances depend on both temperature and emissivity. It is a key problem for us to separate temperature and emissivity in thermal infrared remote sensing re- search. Another difficulty encountered in the retrieval of surface temperature is the correction of downwelling sky irradiance, because it is closely related to surface emissivity. When emissivity is un- known, the downwelling sky irradiance is difficult to be removed. In this paper, we introduce a correc- tion term of downwelling sky irradiance developed by li and Becker into Wien’s approximation, to de- rive an improved ALPHA difference spectrum which is independent of temperature, and furthermore develop a correction term to remove the error of Wien’s approximation. Under the support of the above work, attractive features of Alpha derived emissivity method and ASTER TES algorithm are combined together to acquire a new Improved TES algorithm based on Corrected ALPHA Difference Spectrum (ICADS TES). Because a multi-band inversion technique is applied, and the operations of band ratios and differences are included in the algorithm, it can partly remove the influence of atmosphere and noises. Numerical simulation experiments show that for various combinations of atmosphere, land covers and surface temperatures, the algorithm is applicable and stable. Its accuracy for temperature is 0―1.5 K, and that for emissivity is 0―0.015. Compared with current TES algorithms, our method has clear physical meaning, is easy to be implemented, and is applicable for a wide temperature range and surface types. The results are not influenced by the directional characteristic of emissivity. Because ICADS TES does not need the support of a priori information of surface types, it is also not influenced by the accuracy of classification and the problem of mixture pixels. Compared with our former TES algorithm based on corrected Alpha difference spectra (CADS TES), the new algorithm take

关键词： thermal infrared remote sensing TES algorithm emissivity land surface temperature

新疆地区鸟类和哺乳动物丰富度与环境因子的空间格局与关系

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遥感学报 2019年第1期23卷 155-165页

作者：龙昶宇万华伟李利平王锦地遥感科学国家重点实验室北京市陆表遥感数据产品工程技术研究中心北京师范大学地理科学学部北京100875 环境保护部卫星环境应用中心北京100094 中国科学院遥感与数字地球研究所北京100101

干旱区生态系统极易受到气候及土地利用变化的影响,其生物多样性格局及其形成机制是重要的生态学问题。基于新疆地区鸟类及哺乳动物物种多样性数据,结合气候、地形和长时间序列的植被遥感参数产品FAPAR数据等,主要在不同的土地利用类型... 详细信息

干旱区生态系统极易受到气候及土地利用变化的影响,其生物多样性格局及其形成机制是重要的生态学问题。基于新疆地区鸟类及哺乳动物物种多样性数据,结合气候、地形和长时间序列的植被遥感参数产品FAPAR数据等,主要在不同的土地利用类型及海拔带上采用单因子相关分析方法探讨了物种丰富度格局的形成机制。总体来说,不同生境类型中,植被遥感参数因子(DHI、NDVI等)与两种类群物种丰富度分布的相关性强于与气候因子(温度、降水)的相关性。具体而言,植被遥感参数因子中,基于FAPAR的生境指数因子与丰富度的相关性大于基于植被指数的因子(DHI_cum>NDVI_cum>EVI_cum);气候因子中,在草地生境或者较低的海拔上,年均降水因子对于丰富度分布的解释力强于年均温度因子。这表明在新疆地区,影响鸟类与哺乳类动物物种丰富度分布的主导理论是生境异质性假说与环境稳定性假说,其解释力在多种生境内均强于生产力与环境热量。

关键词：新疆地区物种丰富度格局环境因子指标参数相关关系

发展几何光学遥感建模理论,推动定量遥感科学前行——深切缅怀李小文院士

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遥感学报 2019年第1期23卷 1-10页

作者：柳钦火阎广建焦子锑肖青闻建光梁顺林王锦地中国科学院遥感与数字地球研究所遥感科学国家重点实验室北京100101 中国科学院大学资源与环境学院北京100190 北京师范大学地理科学学部遥感科学国家重点实验室北京100875 马里兰大学地理科学系美国马里兰20742

李小文长期致力于遥感基础研究,是植被几何光学遥感建模的先驱。他创建了li-Strahler几何光学模型,论文入选"国际光学工程学会(SPIE)"里程碑系列文集。以李小文为首席科学家,带领国内外科学团队,在植被二向性反射建模、热红外遥感辐射... 详细信息

李小文长期致力于遥感基础研究,是植被几何光学遥感建模的先驱。他创建了li-Strahler几何光学模型,论文入选"国际光学工程学会(SPIE)"里程碑系列文集。以李小文为首席科学家,带领国内外科学团队,在植被二向性反射建模、热红外遥感辐射方向性建模、遥感信息尺度效应、定量遥感综合实验与真实性检验方面做出了卓越成就。本文总结了李小文的主要学术思想,对定量遥感相关研究进展进行了总结和回顾,对遥感科学及相关领域具有重要的借鉴和参考意义。

关键词：几何光学建模热红外辐射方向性定量遥感反演尺度效应遥感综合试验